For detecting particular conditions in mechanical systems, automatic condition monitoring systems are known which measure and analyse vibrations occurring in the mechanical system. In order to provide reliable monitoring with high safety and a minimum number of false alarms, which could occur due to difficulties in clearly distinguishing between fault frequencies background noise and/or random frequency peaks, it is desirable to optimize the accuracy of the employed automatic evaluation methods in such systems.
Automatic vibration monitoring systems of the state of the art are typically based on one of the following approaches:
A very common approach is to perform rms (root mean square)-measurement pursuant to ISO 10816. Here, the rms typical at a frequency band of 10 to 10000 Hz is calculated, thus obtaining a scalar value that can be continuously compared to a specified threshold level. However, detecting irregular behaviour of the system that has been present initially is not supported by this approach, and neither can the type of damage be characterized in detail.
Another approach is to define a plurality of different rms-bands located at frequencies of particular interest. For instance, frequency spectra such as obtained from FFTs (fast Fourier transforms) or envelope FFTs on envelopes can be used as input data, and the output result is a scalar value for each of the defined bands, which can be compared to threshold levels. Here, damages can be detected at a rather early stage.
However, this approach does not provide for reliable separation of frequencies that lie close to another, so that neighbouring effects significantly impair the reliability of results and thus the applicability of such systems. Further, this approach suffers from a significant sensitivity to variations in background noise or randomly occurring signals in significant frequency bands. Consequently, these systems may produce false alarms and thus provide only a limited specificity. Moreover, such systems typically provide unsatisfactory results in environments where the amplitude level of a fault vibration signature is about equal to the amplitude level of the general background noise of the system.
A further approach is to compare frequency spectra to alarm masks. Such alarm masks can be defined based on reference spectra at the run-in of the mechanical system. Here, typically one or more alarm masks are applied to the reference spectrum. As soon as a mechanical irregularity occurs, such as tooth damages of a gear, sidebands will be produced that pass through the mask and trigger an alarm.
While this approach provides for a fault detection at an even earlier stage than the approach discussed before, it generally suffers from the same limitations as that approach.
A still further approach is proposed in published patent application EP 1 548 419 A1. Here, it is proposed to apply a cepstrum analysis to a frequency spectrum in order to diagnose irregular behaviour of a bearing unit of a railway vehicle axle.
However, such an approach requires a significant extra effort in data processing due to its particular result form. Also, the evaluation of amplitudes in this approach is difficult to reproduce. Here, the provided amplitudes are still less representative than with a plain frequency spectrum.
The limitations of the approaches as described above are of particular significance in automated condition monitoring of wind turbines, particularly of gears or gear boxes of wind turbines. In a wind turbine, there is a number of different vibration components. While some of these components are relevant for monitoring, others are not. Particularly for tooth damages in a planet stage in a wind turbine gearbox, a vibration signal originating from an irregularity is of a low energy content, as compared to the energy content of signals of properly working components or background noise signals. Also in the frequency domain, the signal of any particular of the multitude of the monitored components appears rather mixed with signals of the other components, and also with general background noise. Particularly with advanced monitoring scenarios in wind turbines comprising a multitude of components with similar characteristic vibration frequencies, neighbouring effects are likely to occur.